Interpretive Summary: Applying nitrogen fertilizer when and where needed within a field to meet crop requirements is beneficial to producers in terms of profitability as well as to the environment in terms of reducing potential ground and surface water pollution. Recent advances in sensor technology have enabled researchers to quickly evaluate plant nitrogen status from changes in leaf color. Results have indicated that corn nitrogen stress is accurately detected after full canopy development. Prior to this time, however, sensors have trouble distinguishing leaf color changes from changes in soil background color. This study showed that sensor measurements could be used to determine if corn plants had sufficient size for accurate nitrogen stress detection. An example using sensor measurements to filter out or flag areas of low leaf development prior to classifying the corn plant nitrogen status showed that classification accuracy increased by 34 %.

Technical Abstract:
Soil background effects can easily confound results when using remotely sensed measurements to assess crop parameters. This study focused on finding a non-invasive method of screening low ground-cover areas before assessing crop N status. Four years of plot data were used where intense ground sampling was coupled with nadir-viewing remotely sensed data from a ground-based remote sensing system. Data were collected over irrigated corn (Zea Mays L.) with both light and dark soil backgrounds to develop a relationship between Leaf Area Index (LAI) and remotely sensed data. An index based on subtracting the red from the green reflectance (green-red) was shown to be strongly related to LAI. A concurrent assessment of the N Reflectance Index (NRI) over the four-year study indicated that the corn N status classification improved with increasing LAI values. A minimum performance level for the NRI was then used to determine a minimum (green-red) value for measurements to be considered in the classification. All of the data from the four-year study was then screened by the minimum (green-red) threshold value and the NRI was reassessed. Results indicated that pre-classification screening improved overall NRI accuracy from 0.53 to 0.71 throughout the study. The minimum NRI performance level was set at an r2 = 0.5, which corresponded to removal of measurements from areas exhibiting LAI values less than 2.5. All data were then filtered using a minimum (green-red) value of 0.014 which removed all bare-soil and many of the measurements collected before the V12 growth stage. This method of filtering was shown to be highly effective in screening low-ground cover and bare-soil areas before classification. Future incorporation of this technique into on-the-go filtering algorithms could improve crop N status classification results in early-season and mixed vegetative cover situations.